Marine safety and data analytics : vessel crash stop maneuvering performance prediction
Autor: | Davide Anguita, Andrea Coraddu, Olena Karpenko, Luca Oneto, Paolo Sanetti, Toine Cleophas, Francesca Cipollini |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
Computer science
Data-driven methods VM Vessel maneuvering Crash 02 engineering and technology Shipyard computer.software_genre Performance assessment Performance estimation 0502 economics and business 0202 electrical engineering electronic engineering information engineering Performance prediction Crash stop Marine safety business.industry 05 social sciences Principal (computer security) Random forests Reliability engineering Shipbuilding Key (cryptography) Data analysis 020201 artificial intelligence & image processing Data mining business computer 050203 business & management |
Zdroj: | Artificial Neural Networks and Machine Learning – ICANN 2017 ISBN: 9783319686110 ICANN (2) |
Popis: | Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal. |
Databáze: | OpenAIRE |
Externí odkaz: |
načítá se...